Miscellaneous Functions
Example Usage
The data used in the following examples are simulated using OpenAI’s ChatGPT. Identifiable fields (full names, addresses, etc.) are also simulated.
Using Extracts (> clean_REDCap)
First, we can use a (.csv) extract of the data and data dictionary from REDCap:
dat_raw <- read.csv(file = "materials/miscellaneous/DemoRSyntheticData_DATA_2025-03-18_0728.csv") # Read in the data
dict <- read.csv(file = "materials/miscellaneous/DemoRSyntheticData_DataDictionary_2025-03-18.csv", # Read in the data dictionary
check.names = FALSE ## So spaces, punctuation etc. are preserved in tibble
) Visualising the first few rows of our data:
head(dat_raw, n = 10) %>%
thekids_table(line_spacing = 0.8,
padding = 1.5)
#> Warning in check_font_family(font_family = font_family, fallback_family =
#> fallback_font_family): Font 'Barlow' not found; falling back to 'sans'.record_id | form_1_complete | demo_title | demo_fname | demo_dob | demo_address | demo_postcode | anthro_sex | anthro_weight | anthro_height | assess_iq | demo_edu | demo_income | demo_num_dep | med_hist_cvd | med_hist_diabetes | life_pol | life_house_date | life_super | synthetic_1_complete |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | Mr. | William Moore | 1990‑01‑24 | 99 St Georges Terrace, Scarborough, WA | 6006 | Male | 97.7 | 1.56 | 58 | 3 | 0 | 2 | 0 | 1 | Liberal | 1999‑04‑05 | 4458703 | 0 |
2 | 0 | Mr. | William Brown | 1998‑10‑24 | 6011 | Other | 62.3 | 1.53 | 54 | 1 | 0 | 1 | 0 | 1 | Central | 1985‑05‑31 | 14636346 | 0 | |
3 | 0 | Mr. | Elizabeth Williams | 1983‑03‑01 | 87 Hay Street, Joondalup, WA | 6011 | Male | 146.0 | 2.02 | 119 | 3 | 4 | 6 | 1 | 0 | Liberal | 5334516 | 0 | |
4 | 0 | Ms. | John Miller | 1985‑02‑13 | 85 Hay Street, Perth CBD, WA | 6006 | Female | 66.4 | 1.53 | 54 | 2 | 0 | 5 | 0 | 1 | Labour | 1989‑11‑13 | 6399928 | 0 |
5 | 0 | Mr. | John Brown | 1973‑12‑10 | 33 Beaufort Street, Mount Lawley, WA | 6005 | Other | 114.9 | 1.55 | 57 | 3 | 0 | 6 | 1 | 1 | Labour | 1985‑12‑02 | 294741 | 0 |
6 | 0 | Ms. | Michael Williams | 1997‑01‑29 | 24 St Georges Terrace, Northbridge, WA | 6011 | Female | 69.2 | 1.64 | 69 | 3 | 1 | 2 | 0 | 0 | Labour | 1992‑04‑19 | 10847340 | 0 |
7 | 0 | Ms. | Michael Davis | 1990‑10‑10 | 84 Hay Street, Northbridge, WA | 6007 | Male | 85.6 | 2.03 | 121 | 3 | 4 | 5 | 1 | 0 | Labour | 1991‑04‑28 | 6320541 | 0 |
8 | 0 | Mx. | Mary Brown | 1994‑08‑20 | 85 Murray Street, Mount Lawley, WA | 6008 | Male | 76.7 | 1.70 | 77 | 3 | 1 | 1 | 0 | 0 | Central | 2009‑03‑12 | 1726473 | 0 |
9 | 0 | Ms. | Michael Taylor | 1978‑05‑13 | 29 King Street, Fremantle, WA | 6008 | Other | 109.8 | 66 | 1 | 0 | 0 | 0 | 1 | Liberal | 2004‑03‑05 | 14534640 | 0 | |
10 | 0 | Mx. | Patricia Moore | 1990‑06‑06 | 76 Adelaide Terrace, Cottesloe, WA | 6012 | Other | 139.8 | 1.93 | 107 | 1 | 3 | 7 | 0 | 0 | Liberal | 1992‑12‑20 | 11706012 | 0 |
Similarly, we can visualise our standardised data dictionary extract from REDCap:
Clearly:
- Some categorical fields (
demo_income,demo_num_dep,med_hist_cvd,med_hist_diabetes) are numerically coded and will need resolution before proceeding. - Column headers are variable names, not labels.
If we were to tabulate this data (stratifying by, say,
med_hist_cvd), we must manually apply labels to categorical
variable levels (which is indeed made easier using
thekidsbiostats::fct_case_when) and variable names (passed
directly to tbl_summary):
dat_raw %>%
select(demo_postcode, anthro_sex:life_pol, life_super) %>%
mutate(demo_edu = fct_case_when(demo_edu == 0 ~ "Completed High School",
demo_edu == 1 ~ "TAFE or Trade",
demo_edu == 2 ~ "Higher Degree",
demo_edu == 3 ~ "Bachelor Degree"),
demo_income = fct_case_when(demo_income == 0 ~ "<80000",
demo_income == 1 ~ "80000-120000",
demo_income == 2 ~ "120000-160000",
demo_income == 3 ~ "160000-200000",
demo_income == 4 ~ ">200000"),
across(c(med_hist_cvd, med_hist_diabetes), ~fct_case_when(. == 0 ~ "No",
. == 1 ~ "Yes")),
across(c(anthro_sex, life_pol), ~case_when(. == "" ~ NA,
TRUE ~ .))) %>%
tbl_summary(by = med_hist_cvd,
type = list(demo_postcode ~ "categorical"),
digits = list(all_categorical() ~ c(0, 1)),
label = list(demo_postcode ~ "Postcode",
anthro_sex ~ "Sex",
anthro_weight ~ "Weight",
anthro_height ~ "Height",
assess_iq ~ "IQ",
demo_edu ~ "Highest level of education",
demo_income ~ "Household income",
demo_num_dep ~ "Number of dependents",
med_hist_diabetes ~ "History of diabetes",
life_pol ~ "Political affiliation",
life_super ~ "Total superannuation balance")
) %>%
modify_spanning_header(all_stat_cols() ~ "**History of CVD**") %>%
thekids_table()
#> Warning in check_font_family(font_family = font_family, fallback_family =
#> fallback_font_family): Font 'Barlow' not found; falling back to 'sans'.
#> Factor levels (in order): Completed High School, TAFE or Trade, Higher Degree, Bachelor Degree
#> Factor levels (in order): <80000, 80000-120000, 120000-160000, 160000-200000, >200000
#> Factor levels (in order): No, Yes
#> Factor levels (in order): No, Yes
#> 100 missing rows in the "med_hist_cvd" column have been removed.
| History of CVD | |
|---|---|---|
Characteristic | No | Yes |
Postcode | ||
6000 | 72 (7.9%) | 69 (7.7%) |
6003 | 59 (6.5%) | 79 (8.8%) |
6004 | 85 (9.3%) | 81 (9.0%) |
6005 | 76 (8.3%) | 82 (9.2%) |
6006 | 89 (9.7%) | 90 (10.0%) |
6007 | 85 (9.3%) | 88 (9.8%) |
6008 | 85 (9.3%) | 88 (9.8%) |
6009 | 79 (8.6%) | 81 (9.0%) |
6010 | 82 (9.0%) | 78 (8.7%) |
6011 | 101 (11.1%) | 77 (8.6%) |
6012 | 101 (11.1%) | 83 (9.3%) |
Unknown | 46 | 44 |
Sex | ||
Female | 308 (33.7%) | 272 (30.6%) |
Male | 285 (31.2%) | 314 (35.3%) |
Other | 320 (35.0%) | 304 (34.2%) |
Unknown | 47 | 50 |
Weight | 107 (82, 129) | 106 (85, 129) |
Unknown | 46 | 47 |
Height | 1.79 (1.65, 1.96) | 1.81 (1.66, 1.96) |
Unknown | 44 | 52 |
IQ | 89 (69, 111) | 90 (70, 110) |
Unknown | 50 | 47 |
Highest level of education | ||
Completed High School | 216 (23.6%) | 215 (24.2%) |
TAFE or Trade | 234 (25.5%) | 219 (24.6%) |
Higher Degree | 218 (23.8%) | 230 (25.8%) |
Bachelor Degree | 249 (27.2%) | 226 (25.4%) |
Unknown | 43 | 50 |
Household income | ||
<80000 | 199 (21.7%) | 183 (20.6%) |
80000-120000 | 176 (19.2%) | 169 (19.0%) |
120000-160000 | 186 (20.3%) | 189 (21.3%) |
160000-200000 | 182 (19.9%) | 162 (18.2%) |
>200000 | 173 (18.9%) | 186 (20.9%) |
Unknown | 44 | 51 |
Number of dependents | 3 (2, 5) | 3 (1, 5) |
Unknown | 51 | 45 |
History of diabetes | 471 (51.3%) | 467 (52.6%) |
Unknown | 41 | 53 |
Political affiliation | ||
Central | 301 (33.3%) | 309 (34.3%) |
Labour | 270 (29.9%) | 325 (36.0%) |
Liberal | 332 (36.8%) | 268 (29.7%) |
Unknown | 57 | 38 |
Total superannuation balance | 7,456,367 (3,930,725, 11,271,067) | 7,679,442 (3,868,717, 11,056,757) |
Unknown | 42 | 53 |
1n (%); Median (Q1, Q3) | ||
The above code chunk is clearly quite sizeable and required a significant degree of user input.
Instead, using clean_REDCap:
dat_mod <- clean_REDCap(d = dat_raw,
dict = dict,
yesno_to_bool = T)
dat_mod %>%
select(demo_postcode, anthro_sex:life_pol, life_super) %>%
tbl_summary(by = med_hist_cvd,
type = list(demo_postcode ~ "categorical"),
digits = list(all_categorical() ~ c(0, 1))
) %>%
modify_spanning_header(all_stat_cols() ~ "**History of CVD**") %>%
thekids_table()
#> Warning in check_font_family(font_family = font_family, fallback_family =
#> fallback_font_family): Font 'Barlow' not found; falling back to 'sans'.
#> 100 missing rows in the "med_hist_cvd" column have been removed.
| History of CVD | |
|---|---|---|
Characteristic | No | Yes |
Postcode | ||
6000 | 72 (7.9%) | 69 (7.7%) |
6003 | 59 (6.5%) | 79 (8.8%) |
6004 | 85 (9.3%) | 81 (9.0%) |
6005 | 76 (8.3%) | 82 (9.2%) |
6006 | 89 (9.7%) | 90 (10.0%) |
6007 | 85 (9.3%) | 88 (9.8%) |
6008 | 85 (9.3%) | 88 (9.8%) |
6009 | 79 (8.6%) | 81 (9.0%) |
6010 | 82 (9.0%) | 78 (8.7%) |
6011 | 101 (11.1%) | 77 (8.6%) |
6012 | 101 (11.1%) | 83 (9.3%) |
Unknown | 46 | 44 |
Sex | ||
47 (4.9%) | 50 (5.3%) | |
Female | 308 (32.1%) | 272 (28.9%) |
Male | 285 (29.7%) | 314 (33.4%) |
Other | 320 (33.3%) | 304 (32.3%) |
Weight | 107 (82, 129) | 106 (85, 129) |
Unknown | 46 | 47 |
Height | 1.79 (1.65, 1.96) | 1.81 (1.66, 1.96) |
Unknown | 44 | 52 |
IQ | 89 (69, 111) | 90 (70, 110) |
Unknown | 50 | 47 |
Highest Level of Education | ||
Completed High School | 216 (23.6%) | 215 (24.2%) |
TAFE or Trade | 234 (25.5%) | 219 (24.6%) |
Higher Degree | 218 (23.8%) | 230 (25.8%) |
Bachelor Degree | 249 (27.2%) | 226 (25.4%) |
Unknown | 43 | 50 |
Household Income | ||
<80000 | 199 (21.7%) | 183 (20.6%) |
80000-120000 | 176 (19.2%) | 169 (19.0%) |
120000-160000 | 186 (20.3%) | 189 (21.3%) |
160000-200000 | 182 (19.9%) | 162 (18.2%) |
>200000 | 173 (18.9%) | 186 (20.9%) |
Unknown | 44 | 51 |
Number of Dependents | 3 (2, 5) | 3 (1, 5) |
Unknown | 51 | 45 |
History of Diabetes | 471 (51.3%) | 467 (52.6%) |
Unknown | 41 | 53 |
Political Affiliation | ||
57 (5.9%) | 38 (4.0%) | |
Central | 301 (31.4%) | 309 (32.9%) |
Labour | 270 (28.1%) | 325 (34.6%) |
Liberal | 332 (34.6%) | 268 (28.5%) |
Total Super Balance | 7,456,367 (3,930,725, 11,271,067) | 7,679,442 (3,868,717, 11,056,757) |
Unknown | 42 | 53 |
1n (%); Median (Q1, Q3) | ||
The variable levels and labels are automatically applied.